Admixture mapping of cognitive function in diverse Hispanic and Latino adults: Results from the Hispanic Community Health Study/Study of Latinos.

INTRODUCTION: We conducted admixture mapping and fine-mapping analyses to identify ancestry-of-origin loci influencing cognitive abilities. METHODS: We estimated the association of local ancestry intervals across the genome with five neurocognitive measures in 7140 diverse Hispanic and Latino adults (mean age 55 years). We prioritized genetic variants in associated loci and tested them for replication in four independent cohorts. RESULTS: We identified nine local ancestry-associated regions for the five neurocognitive measures. There was strong biological support for the observed associations to cognitive function at all loci and there was statistical evidence of independent replication at 4q12, 9p22.1, and 13q12.13. DISCUSSION: Our study identified multiple novel loci harboring genes implicated in cognitive functioning and dementia, and uncovered ancestry-relevant genetic variants. It adds to our understanding of the genetic architecture of cognitive function in Hispanic and Latino adults and demonstrates the power of admixture mapping to discover unique haplotypes influencing cognitive function, complementing genome-wide association studies. HIGHLIGHTS: We identified nine ancestry-of-origin chromosomal regions associated with five neurocognitive traits. In each associated region, we identified single nucleotide polymorphisms (SNPs) that explained, at least in part, the admixture signal and were tested for replication in independent samples of Black, non-Hispanic White, and Hispanic/Latino adults with the same or similar neurocognitive tests. Statistical evidence of independent replication of the prioritized SNPs was observed for three of the nine associations, at chr4q12, chr9p22.1, and chr13q12.13. At all loci, there was strong biological support for the observed associations to cognitive function and dementia, prioritizing genes such as KIT, implicated in autophagic clearance of neurotoxic proteins and on mast cell and microglial-mediated inflammation; SLC24A2, implicated in synaptic plasticity associated with learning and memory; and MTMR6, implicated in phosphoinositide lipids metabolism.

Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study.

PURPOSE: The study was aimed to develop and evaluate a deep learning-based radiomics to predict the histological risk categorization of thymic epithelial tumors (TETs), which can be highly informative for patient treatment planning and prognostic assessment. METHOD: A total of 681 patients with TETs from three independent hospitals were included and separated into derivation cohort and external test cohort. Handcrafted and deep learning features were extracted from preoperative contrast-enhanced CT images and selected to build three radiomics signatures (radiomics signature [Rad_Sig], deep learning signature [DL_Sig] and deep learning radiomics signature [DLR_Sig]) to predict risk categorization of TETs. A deep learning-based radiomic nomogram (DLRN) was then depicted to visualize the classification evaluation. The performance of predictive models was compared using the receiver operating characteristic and decision curve analysis (DCA). RESULTS: Among three radiomics signatures, DLR_Sig demonstrated optimum performance with an AUC of 0.883 for the derivation cohort and 0.749 for the external test cohort. Combining DLR_Sig with age and gender, DLRN was depict and exhibited optimum performance among all radiomics models with an AUC of 0.965, accuracy of 0.911, sensitivity of 0.921 and specificity of 0.902 in the derivation cohort, and an AUC of 0.786, accuracy of 0.774, sensitivity of 0.778 and specificity of 0.771 in the external test cohort. The DCA showed that DLRN had greater clinical benefit than other radiomics signatures. CONCLUSIONS: Our study developed and validated a DLRN to accurately predict the risk categorization of TETs, which has potential to facilitate individualized treatment and improve patient prognosis evaluation.

Evaluation of outcome measures for myasthenia gravis subgroups.

INTRODUCTION: Disease evaluation and long-term follow-up of myasthenia gravis (MG) patients rely on disease-specific measures. We evaluated four widely used MG-specific assessments, and compared the response to disease change in different MG subgroups. METHODS: We used the Cronbach's α coefficient to test reliability, Pearson correlation coefficients to test construct validity, as well as one-way ANOVA and independent-sample t-tests to access discriminant validity. Analyses of similar items between QMG and MG-ADL included paired-sample t-tests and mean score comparisons. Pearson correlation coefficients were used to describe the correlation between changes of QMG, MG-ADL, MG-QOL15r and MGC. The Wilcoxon matched-pairs signed-ranks test was performed to compare the outcomes. RESULTS: 872 MG patients were enrolled. QMG, MG-ADL, MG-QOL15r, and MGC all exhibited high reliability. All four scales displayed good discriminant validity according to the MGFA classification and MGC score. MG-ADL showed significant differences between patients grouped by age and gender, and MG-QOL15r showed significant differences between patients grouped by age. Analyses of similar items showed that MG-ADL achieved higher scores in bulbar items, whereas QMG produced higher scores in limb items. For patients in remission or minimal manifestation status, QMG exhibited significantly greater improvement than MG-QOL15r. In patients of MGFA I, II, III, and IV, QMG showed significantly greater improvement than MG-ADL. CONCLUSIONS: Patient-reported scale is an important supplement for a given period. MG-ADL has a better response to severe disease, and MG-QOL15r is more comprehensive for patients in remission or minimal manifestation status.

B2M overexpression correlates with malignancy and immune signatures in human gliomas.

Because of the limited treatment strategy of gliomas, the key of diagnosis and treatment is finding new molecular biomarkers. Here, we explored the potential of ß2-microglobulin (B2M) to serve as a hopeful candidate for immunotherapy or diagnostic biomarker in gliomas. The genomic profiles, clinical characteristics, and immune signatures were analyzed based on TCGA and CGGA databases. We carried out the whole statistical analyses using R project. High B2M expression correlated with worse prognosis. Somatic mutations of gliomas with high B2M expression are associated with PTEN deletion and EGFR amplification. Isocitrate dehydrogenase (IDH) mutations accounted for 82% in gliomas with low B2M expression. In addition, B2M positively correlated with ESTIMATE scores, interacted with infiltrating immune and stromal cell types. B2M also suppressed anti-tumor immunity through immune related processes. Meanwhile, B2M was associated with immune checkpoint molecules and inflammatory activities. Finally, functional annotation of the identified B2M related genes verified that B2M was a potential candidate for immunotherapy. We confirmed that B2M played a critical role in tumor progression, patient prognosis and immunotherapy of gliomas.

PDIA3 correlates with clinical malignant features and immune signature in human gliomas.

Since therapeutic strategies are limited in gliomas, new molecules or biomarkers are essential for diagnosis and therapy. Here, we investigated expression of protein disulfide isomerase family A member 3 (PDIA3) in gliomas to evaluate its potential as a promising immune target or biomarker. Transcriptome level, genomic profiles and its association with clinical practice from TCGA and CGGA databases were analyzed. All statistical analyses were performed using R project. In gliomas with high PDIA3 expression, somatic mutations showed the correlation with loss of PTEN and amplification of EGFR; meanwhile, in PDIA3 low gliomas, mutations in isocitrate dehydrogenase (IDH) took 80%. Moreover, PDIA3 was found to positively correlate with ESTIMATE scores and diverse infiltrating immune and stromal cell types localizing in tumor microenvironment. PDIA3 was found to be highly correlated with macrophage and T cells based on single cell sequencing. Additionally, PDIA3 was also involved in suppression of anti-tumor immunity via multiple immune regulatory processes. Finally, PDIA3 was observed to correlate with other immune checkpoint inhibitors and associated with inflammation. Our findings identified the significance of PDIA3 in the process of gliomas and demonstrated the potential of PDIA3 as a molecular target in prognosis and immune related treatment of gliomas.

Novel insights into astrocyte-mediated signaling of proliferation, invasion and tumor immune microenvironment in glioblastoma.

Glioblastoma (GBM) continues to be the most aggressive cancer of the brain. The dismal prognosis is largely attributed to the microenvironment surrounding tumor cells. Astrocytes, the main component of the GBM microenvironment, play several fundamental physiological roles in the central nervous system. During the development of GBM, tumor-associated astrocytes (TAAs) directly contact GBM cells, which activate astrocytes to form reactive astrocytes, facilitating tumor progression, proliferation and migration through multiple well-understood signaling pathways. Notably, TAAs also influence GBM cell behaviors via suppressing immune responses and enhancing the chemoradiotherapy resistance of tumor cells. These new activities are closely linked with the treatment and prognosis of GBM. In this review, we discuss recent advances regarding new functions of reactive astrocytes, including TAA-cancer cell interactions, mechanisms involved in immunosuppressive regulation, and chemoradiotherapy resistance. It is expected that these updated experimental or clinical studies of TAAs may provide a promising approach for GBM treatment in the near future.